Our group includes PostDocs, PhD students, and student assistants, and is headed by Prof. Dr. Hasso Plattner. If you are interested in our work or want to join our team, please contact Dr. Matthias Uflacker.

Our team is giving a series of lectures and seminars with a focus on enterprise systems design and in-memory data management. Strong links to the industry ensure a close connection between theory and its implementation in the real world.

Our research focuses on the principles of in-memory data management on modern hardware and the integration of different hard- and software systems to meet business requirements. This involves studying the conceptual and technological aspects of modern enterprise applications as well as tools and methods for enterprise systems design.

We continually strive to translate our research into practical outputs that improve the quality of enterprise applications. A close link to industry partners ensures relevance and impact of our work. Get here an overview of our current and previous projects.

Christian Schwarz, M. Sc.

Research Projects

Predictive Analytics on In-Memory Databases

For manufacturers it is important to have an accurate demand forecast for their products in order to avoid over or under capacity in their stores. In case of Vendor-Managed-Inventory the manufacturer is solely responsible for filling the shelfs inside the retail stores. Point-of-Sale (POS) data is one of the most important basis for forecasting. However, for different reasons, many shops cannot provide this kind of data. Instead of using imprecise shipment forecasting, new approaches have to be evaluated.

Intermodal Mobility using In-Memory Databases

The usage of electric vehicles is especially attractive for people living urban areas. Those people often only have to drive short distances and are able to charge their electric vehicles at home. Thus, the limited travel distance does not negatively affect the overall comfort of owning an electric vehicle vs. using a normal car. Nevertheless, in larger cities like Berlin, the range provided by one charging cycle might not be enough for one day. For drivers of electrical vehicles it got complicated if they need to recharge their vehicle during a trip, requiring up to multiple hours for recharging their vehicle. This project has build a prototype to make it more comfortable to drive an EV, even when recharging is required.

In-Memory Real-Time Energy Management

The project focuses on the real-time evaluation and processing of huge amounts of data that arise from smart grids, both for enterprises as well as customers since smart homes and smart industries leverage great possibilities for the existing challenges in the energy business. In-memory column store technology allows us to process the huge amount of data in real time, including energy demand forecasting and consumption pattern analysis.

The Rock Project

For traditional data warehouses, mostly large and expensive server and storage systems are used. In particular, for small- and medium size companies, it is often too expensive to run or rent such systems. This problem stems from the use of a) complex cube structures containing pre-aggregated values for reporting and b) materialized views to pre-compute joins between fact and dimensions tables. The inherent design principles of memory-based column databases allow for the computation of aggregations and joins on-the-fly without relying on materialized views, making them the technology of choice for SME analytics. SMEs might, however, need analytical services only from time to time, for example at the end of a billing period. A solution to overcome these problems is to use Cloud Computing. In the Rock project, we are building an OLAP cluster of analytics databases on the Amazon EC2 cloud. For this purpose we build infrastructure around SAP's in-memory column database TREX to support multi-tenancy, replication, and failover. This project is joint work with SAP and the University of California in Berkeley.

Open Positions

We are proud to announce " A Course in In-Memory Data Management" by Prof. Dr. h.c. Hasso Plattner. This book is the culmination of six years work of in-memory research. As such, it provides the technical foundation for combined transactional and analytical workloads inside one single database as well as examples of new applications that are now possible given the availability of the new technology. The book is available at Springer.